https://github.com/pcpp94/prophet_gb_demand_forecasting_one

This repository provides tools for forecasting Great Britain’s power demand using Facebook’s Prophet model. It includes Python functions and Jupyter notebooks for daily and monthly forecasts, integrating weather and GDP as regressors, with hyperparameter tuning via HyperOpt.
https://github.com/pcpp94/prophet_gb_demand_forecasting_one

bayesian-inference bayesian-optimization daily decomposition demand energy forecast forecasting holidays hourly mathematics monthly power prophet time-series time-series-analysis timeseries weather

Last synced: about 17 hours ago
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This repository provides tools for forecasting Great Britain’s power demand using Facebook’s Prophet model. It includes Python functions and Jupyter notebooks for daily and monthly forecasts, integrating weather and GDP as regressors, with hyperparameter tuning via HyperOpt.

• Host: GitHub
• URL: https://github.com/pcpp94/prophet_gb_demand_forecasting_one
• Owner: pcpp94
• Created: 2024-09-23T19:21:47.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-10-17T13:07:05.000Z (3 months ago)
• Last Synced: 2024-11-28T09:19:36.774Z (2 months ago)
• Topics: bayesian-inference, bayesian-optimization, daily, decomposition, demand, energy, forecast, forecasting, holidays, hourly, mathematics, monthly, power, prophet, time-series, time-series-analysis, timeseries, weather
• Language: Jupyter Notebook
• Homepage:
• Size: 67.9 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# GB Power Demand Forecasting using Prophet

## Description

This repository provides tools for forecasting the power demand of Great Britain (GB) using Facebook's [Prophet](https://facebook.github.io/prophet/) model. The repository includes Python functions and Jupyter notebooks that illustrate how to apply Prophet for daily and monthly forecasting, quantifying the effect of weather and other regressors (e.g., GDP), and using HyperOpt for tuning hyperparameters.

### Key Features:

- **Daily and Monthly Power Demand Models**: Separate models for short-term (daily) and long-term (monthly) forecasting.

- **Weather and GDP Regressors**: Integration of weather data and GDP as external regressors to improve forecasting accuracy.

- **Hyperparameter Tuning with HyperOpt**: Automated tuning of Prophet's hyperparameters for optimized performance.

- **Comprehensive Notebooks**: Detailed notebooks explaining the internals of Prophet, including mathematical foundations.

- **Presentation**: A presentation explaining all the key takeaways and also the mathematical foundations behind Prophet.

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## Usage

- In the modelling folder you can find notebooks that take you step-by-step through the modelling process.

- The Powerpoint presentation presents the mathematical foundations in a clear way and also the main takeaways and explanation of different variables/parameters.